How is the percentage or the probablity calculated using Loss function in Facenet Model?
The facenet model is just the head of the model. The architecture is similar to the enocdr part of an autoencoder, but it uses supervised learning instead of unsupervised learning. The network is called a siamese network The triplet loss helps make the embeddings more representative of the input image/person, with the embedding distance being as large as possible for difference people and vice versa. 

However the embeddings is just the representation of the people. It doesn't contain information directly mapping to what person it is. The classification head is used after the Face Net feature extraction top is used.

One-shot learning method for person classfication

This method saves the calculated embeddings of people in a database. The face recognition system first calculates the embeddings of the new, unknown image to be classified. The system loop through the embeddings database and calculates the Euclidean distance of the unknown embeddings and the embeddings in the database. 

After computing, it chooses the smallest distance of all and compares it to a threshold set by you. If the distance is larger than the threshold, the classified class is unknown , or else the resulting class will be the embeddings which results to the lowest distance.

Example code from the deeplearning.ai coursera course (great course on AI btw):
Example code of one shot face recognition

Advantages and disadvantages of the system

The system have advantages ranging from no training needed and can classify unknown class. However, once the number of people in the database becomes huge, the system will run slower. It's performance may also differ depending on the quality of the reference image.

SVM (Support Vector Machine) method

This is the method used in the github repository of your previous posts. It uses a one layer nn classifying head to output the classified class.

Advantages and disadvantages of the system

The system have the flexibility of adapting to multiple photo environment and no affecting of performance due to reference images. This method also works well for larger database of people. Triplet loss can also be removed using this method. You can train the network directly from propagating the loss with the probability predicted. However, This method requires re-training when a new person is added to the system. Multiple images of a person is also needed.